The application of blackbox ML models to high stakes' decisions such as
those involved in healthcare, legal judgment, critical infrastructure
management has been challenged by researchers, e.g.,
[1]}, [2]}, [3]}.
Today's evaluations conducted on DL models are based on a certain split
of training and test data, which are collected using the same protocol.
Such an evaluation methodology could be too simple to be trustworthiness.
Conclusions drawn from a set of input-output relationships can be
misleading and counter intuition. It is unclear how to quantify the
prediction performance on unseen data. It is difficult to provide a
logical description of the decision process to experts in the field to
win their trust. For GL to be well accepted by the society, especially
those with severe consequences, it should be an interpretable discipline
validated by scientific rigor. Since GL is statistically and
mathematically rooted, it will yield probabilistic models that have
performance guarantees and allow risk assessment.
